Despite these advancements, even adaptive comfort models follow a one-size-fits-all approach that ignores the personal aspect of comfort; this is like expecting everyone to have similar preferences for food, music, or style; all subjective attributes of a person's personality. Further work on adaptive models is needed to identify comfort preferences on an individual basis and not only based on the thermal conditions. Recent studies have shown that occupants exposed to the same conditions could exhibit variations in environmental perception due to individual differences in comfort preferences and personality [1]}, [2]}. Researchers have addressed this through the development of personal comfort models that predict individual thermal comfort responses rather than the average response of a larger population [3]} through leveraging machine learning techniques and the Internet of Things (IoT) technologies. This approach demonstrates a very high prediction accuracy, well beyond that of the traditional models [4]}.
